Structural plasticity on an accelerated analog neuromorphic hardware
system
- URL: http://arxiv.org/abs/1912.12047v2
- Date: Wed, 30 Sep 2020 08:20:35 GMT
- Title: Structural plasticity on an accelerated analog neuromorphic hardware
system
- Authors: Sebastian Billaudelle, Benjamin Cramer, Mihai A. Petrovici, Korbinian
Schreiber, David Kappel, Johannes Schemmel, Karlheinz Meier
- Abstract summary: We present a strategy to achieve structural plasticity by constantly rewiring the pre- and gpostsynaptic partners.
We implemented this algorithm on the analog neuromorphic system BrainScaleS-2.
We evaluated our implementation in a simple supervised learning scenario, showing its ability to optimize the network topology.
- Score: 0.46180371154032884
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In computational neuroscience, as well as in machine learning, neuromorphic
devices promise an accelerated and scalable alternative to neural network
simulations. Their neural connectivity and synaptic capacity depends on their
specific design choices, but is always intrinsically limited. Here, we present
a strategy to achieve structural plasticity that optimizes resource allocation
under these constraints by constantly rewiring the pre- and gpostsynaptic
partners while keeping the neuronal fan-in constant and the connectome sparse.
In particular, we implemented this algorithm on the analog neuromorphic system
BrainScaleS-2. It was executed on a custom embedded digital processor located
on chip, accompanying the mixed-signal substrate of spiking neurons and synapse
circuits. We evaluated our implementation in a simple supervised learning
scenario, showing its ability to optimize the network topology with respect to
the nature of its training data, as well as its overall computational
efficiency.
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